Tools and techniques for social science simulation
著者
書誌事項
Tools and techniques for social science simulation
Physica-Verlag, c2000
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注記
Includes bibliographical references and indexes
内容説明・目次
内容説明
The use of computer simulations to study social phenomena has grown rapidly during the last few years. Many social scientists from the fields of economics, sociology, psychology and other disciplines now use computer simulations to study a wide range of social phenomena. The availability of powerful personal computers, the development of multidisciplinary approaches and the use of artificial intelligence models have all contributed to this development. The benefits of using computer simulations in the social sciences are obvious. This holds true for the use of simulations as tools for theory building and for its implementation as a tool for sensitivity analysis and parameter optimization in application-oriented models. In both, simulation provides powerful tools for the study of complex social systems, especially for dynamic and multi-agent social systems in which mathematical tractability is often impossible. The graphical display of simulation output renders it user friendly to many social scientists that lack sufficient familiarity with the language of mathematics. The present volume aims to contribute in four directions: (1) To examine theoretical and methodological issues related to the application of simulations in the social sciences. By this we wish to promote the objective of designing a unified, user-friendly, simulation toolkit which could be applied to diverse social problems. While no claim is made that this objective has been met, the theoretical issues treated in Part 1 of this volume are a contribution towards this objective.
目次
Simulations as Tools for Modeling and Theory Building: N. Gilbert: Models, Processes and Algorithms: Towards a Simulation Toolkit.- J. Doran: Questions in the Methodology of Artificial Societies.- G. Muller: Computer Assisted Interfacing: On the Use of Computer Simulation for Theory Construction.- C. Stoica: Interactive Neural Networks as Tools for Modeling Social Systems.- Multi-Agent Based Simulations: W. Balzer: SMASS: A Serial Multi-Agent System for Social Simulation.- C. Urban: PECS: A Reference Model for the Simulation of Multi-Agent Systems.- M. Rockloff: Building Multi-Agent Simulation Applications.- R. Conte: Diversity in Strategies of Partnership Formation.- Game Theory and Cellular Automata: R. Hegselmann, A. Flache, V. Moeller: Cellular Automata Models of Solidarity and Opinion Formation: Sensitivity Analysis.- O. Kirchkamp: Evolution of Learning Rules in Space.- B. Latane: Simulating the Role of Nonlinearity and Discreteness in Dynamic Social Impact.- I. Fischer, R. Suleiman: The Emergence of Mutual Cooperation in a Simulated Inter-Group Conflict.- Sensitivity Analysis: E. Chattoe, N. Saam, M. Moehring: Sensitivity Analysis in the Social Sciences.- C. Schatz: Tests of Dynamic Social Models with Time Related Surveys: an Experimental Approach.- M. Paolucci, M. Marsero, R. Comte: What is the Use of Gossip? A Sensitivity Analysis of the Spreading of Respectful Reputation.- Applications: K.H. Brassel, O. Edenhofer, M. Moehring, K.G. Troitzsch: Economic Development, Opinion Formation, and Technological Change in a Multilevel Simulation Model.- P. Kokic, R. Chambers, S. Beare: Microsimulating Farm Business Performance.- J. Kluver, J. Schmidt, R. Kier: Ordering Parameters in the Rule Space ofSocial Systems: Searching for a Universal Grammar of Social Actions.
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